{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>EDU_EXPERIENCE</th>\n",
       "      <th>WORK_SIZE</th>\n",
       "      <th>WORK_POWER</th>\n",
       "      <th>IS_ILLEGAL_HIS</th>\n",
       "      <th>CURR_FREEZE_VALUE</th>\n",
       "      <th>GRADUATE_YEAR</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>VIP_FLAG</th>\n",
       "      <th>GRAY_FLAG</th>\n",
       "      <th>FIVE_CLASS_TYPE</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.0</td>\n",
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       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "       AGE  GENDER  MARRIAGE  EDU_EXPERIENCE  WORK_SIZE  WORK_POWER  \\\n",
       "15735   51     1.0       2.0            99.0          2         1.0   \n",
       "15741   56     1.0       2.0            60.0          2         1.0   \n",
       "15753   45     1.0       2.0            70.0          2         1.0   \n",
       "15788   41     1.0       2.0            70.0          2         1.0   \n",
       "15797   42     1.0       3.0            70.0          3         1.0   \n",
       "\n",
       "       IS_ILLEGAL_HIS  CURR_FREEZE_VALUE  GRADUATE_YEAR  OCCUPATION  \\\n",
       "15735             2.0                0.0            4.0         9.0   \n",
       "15741             2.0                0.0            4.0         9.0   \n",
       "15753             2.0                0.0            3.0         9.0   \n",
       "15788             2.0                0.0            4.0         9.0   \n",
       "15797             2.0                0.0            4.0         9.0   \n",
       "\n",
       "      OCCUPATION_TYPE  VIP_FLAG  GRAY_FLAG  FIVE_CLASS_TYPE  \n",
       "15735               5         0          0              0.0  \n",
       "15741               5         0          0              0.0  \n",
       "15753               z         0          0              0.0  \n",
       "15788               z         0          0              0.0  \n",
       "15797               5         0          0              1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path='./test.csv'\n",
    "data = pd.read_csv(data_path,index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "AGE\t年龄\n",
    "GENDER\t性别\n",
    "MARRIAGE\t结婚\n",
    "EDU_EXPERIENCE\t教育经验\n",
    "WORK_SIZE\t工作年限\n",
    "WORK_POWER\t工作级别\n",
    "IS_ILLEGAL_HIS\t是否非法\n",
    "CURR_FREEZE_VALUE\t当前冻结值\n",
    "GRADUATE_YEAR\t毕业年度\n",
    "OCCUPATION\t职业\n",
    "OCCUPATION_TYPE\t职业类型\n",
    "VIP_FLAG\tVIP\n",
    "GRAY_FLAG\t灰色\n",
    "FIVE_CLASS_TYPE\t五级分类\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['年龄',\n",
       " '性别',\n",
       " '结婚',\n",
       " '最高学历',\n",
       " '劳动人口数',\n",
       " '劳动能力',\n",
       " '是否非法',\n",
       " '账户冻结金额',\n",
       " '工作年限',\n",
       " '职务',\n",
       " '职业类型',\n",
       " '白名单客户',\n",
       " '灰名单客户',\n",
       " '五级分类']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns='年龄\t性别\t结婚\t最高学历\t劳动人口数\t劳动能力\t是否非法\t账户冻结金额\t工作年限\t职务\t职业类型\t白名单客户\t灰名单客户\t五级分类'.split('\\t')\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
       "      <th>性别</th>\n",
       "      <th>结婚</th>\n",
       "      <th>最高学历</th>\n",
       "      <th>劳动人口数</th>\n",
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       "    <tr>\n",
       "      <th>15797</th>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "       年龄   性别   结婚  最高学历  劳动人口数  劳动能力  是否非法  账户冻结金额  工作年限   职务 职业类型  白名单客户  \\\n",
       "15735  51  1.0  2.0  99.0      2   1.0   2.0     0.0   4.0  9.0    5      0   \n",
       "15741  56  1.0  2.0  60.0      2   1.0   2.0     0.0   4.0  9.0    5      0   \n",
       "15753  45  1.0  2.0  70.0      2   1.0   2.0     0.0   3.0  9.0    z      0   \n",
       "15788  41  1.0  2.0  70.0      2   1.0   2.0     0.0   4.0  9.0    z      0   \n",
       "15797  42  1.0  3.0  70.0      3   1.0   2.0     0.0   4.0  9.0    5      0   \n",
       "\n",
       "       灰名单客户  五级分类  \n",
       "15735      0   0.0  \n",
       "15741      0   0.0  \n",
       "15753      0   0.0  \n",
       "15788      0   0.0  \n",
       "15797      0   1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_cached = data.columns\n",
    "data.columns=columns\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x89ca630>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[data['五级分类']==0,'年龄'].hist(bins=20,alpha=0.5,label='good')\n",
    "data.loc[data['五级分类']==1,'年龄'].hist(bins=20,label='bad')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8a196d8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['年龄','五级分类']].boxplot(by='五级分类')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8b3f240>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['性别'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'五级分类'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mD:\\annocada\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3077\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '五级分类'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-837d25233dc9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'五级分类'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'性别'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\annocada\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2686\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2690\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\annocada\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2693\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2695\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2697\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\annocada\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2489\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\annocada\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\annocada\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3078\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '五级分类'"
     ]
    }
   ],
   "source": [
    "data.loc[data['五级分类']==0,'性别']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8b7cb38>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['结婚'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c1b5f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['最高学历'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c90be0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['劳动人口数'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8cf2c50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['劳动能力'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 这个变量需要删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d54d68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['是否非法'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d87f28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['账户冻结金额'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c1b630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['工作年限'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e6deb8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['职务'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d4a0b8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAADWJJREFUeJzt3H+M5HV9x/HnC05JrUYgd1DK0Z5trxWMFel6mFITLCkCNsU2pYW2eiG016ZHLKlpudo/xCY0pIk1kljMVRBIKki1BNqSKrmKljQKi1F+nZRTEa78WqoBU4xy+O4f+724PfZul92Zm903z0eymZnPfOc7b47b537vuzOTqkKS1Nchkx5AkjRehl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnNrJj0AwNq1a2vDhg2THkOSVpW77rrrqapat9B2KyL0GzZsYHp6etJjSNKqkuSbi9nOUzeS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4ZekppbEW+Ykjr48B/9+8j3ufUjvzzyfeqlxyN6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNbdg6JMcl+SzSXYmuS/JnwzrRya5NcmDw+URw3qSXJ5kV5K7k5w07v8ISdL+LeaIfg/wnqo6HngzsDXJCcA2YEdVbQR2DLcBzgQ2Dl9bgCtGPrUkadEWDH1VPVZVXxqufwfYCRwLnA1cM2x2DfCO4frZwLU16wvA4UmOGfnkkqRFeVHn6JNsAN4IfBE4uqoeg9kfBsBRw2bHAo/MedjuYU2SNAGLDn2SVwKfAi6qqmcOtOk8azXP/rYkmU4yPTMzs9gxJEkv0qJCn+RlzEb+H6rqn4blJ/aekhkunxzWdwPHzXn4euDRffdZVduraqqqptatW7fU+SVJC1jMq24CXAnsrKq/nXPXzcDm4fpm4KY56+8aXn3zZuDpvad4JEkH35pFbHMK8E7gniRfHtbeC1wG3JDkAuBh4JzhvluAs4BdwLPA+SOdWJL0oiwY+qq6nfnPuwOcNs/2BWxd5lySpBHxnbGS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqbkFQ5/kqiRPJrl3ztolSf47yZeHr7Pm3PcXSXYleSDJ28Y1uCRpcRZzRH81cMY86x+sqhOHr1sAkpwAnAu8bnjM3yU5dFTDSpJevAVDX1WfB761yP2dDVxfVd+rqm8Au4BNy5hPkrRMyzlHf2GSu4dTO0cMa8cCj8zZZvewJkmakKWG/grgp4ETgceADwzrmWfbmm8HSbYkmU4yPTMzs8QxJEkLWVLoq+qJqnq+qn4A/D0/PD2zGzhuzqbrgUf3s4/tVTVVVVPr1q1byhiSpEVYUuiTHDPn5q8De1+RczNwbpLDkrwG2AjcsbwRJUnLsWahDZJcB5wKrE2yG3gfcGqSE5k9LfMQ8IcAVXVfkhuA+4E9wNaqen48o0uSFmPB0FfVefMsX3mA7S8FLl3OUJKk0fGdsZLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0tGPokVyV5Msm9c9aOTHJrkgeHyyOG9SS5PMmuJHcnOWmcw0uSFraYI/qrgTP2WdsG7KiqjcCO4TbAmcDG4WsLcMVoxpQkLdWCoa+qzwPf2mf5bOCa4fo1wDvmrF9bs74AHJ7kmFENK0l68ZZ6jv7oqnoMYLg8alg/Fnhkzna7h7UXSLIlyXSS6ZmZmSWOIUlayKh/GZt51mq+Datqe1VNVdXUunXrRjyGJGmvpYb+ib2nZIbLJ4f13cBxc7ZbDzy69PEkScu11NDfDGwerm8Gbpqz/q7h1TdvBp7ee4pHkjQZaxbaIMl1wKnA2iS7gfcBlwE3JLkAeBg4Z9j8FuAsYBfwLHD+GGaWJL0IC4a+qs7bz12nzbNtAVuXO5QkaXR8Z6wkNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmlvwY4olSft3ySWXrPh9ekQvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktTcmuU8OMlDwHeA54E9VTWV5EjgE8AG4CHgt6rq28sbU5K0VMsK/eCtVfXUnNvbgB1VdVmSbcPti0fwPHqJ2vna40e+z+O/unPk+5RWqlGEfl9nA6cO168BbsPQr0ivv+b1I9/nPZvvGfk+JS3Pcs/RF/CZJHcl2TKsHV1VjwEMl0fN98AkW5JMJ5memZlZ5hiSpP1Z7hH9KVX1aJKjgFuTfHWxD6yq7cB2gKmpqVrmHJKk/VjWEX1VPTpcPgncCGwCnkhyDMBw+eRyh5QkLd2SQ5/kR5O8au914HTgXuBmYPOw2WbgpuUOKUlauuWcujkauDHJ3v18vKr+LcmdwA1JLgAeBs5Z/piSpKVacuir6uvAG+ZZ/x/gtOUMJUkaHd8ZK0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5sbxoWaStGy7t/3HyPe5/rK3jHyfq4FH9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5lbdRyBs2PavI9/nQ5e9feT7lKSVwiN6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqblV9zr6VeOSV494f0+Pdn96yfrAb//qyPf5nk/8y8j3qdHxiF6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1NzYQp/kjCQPJNmVZNu4nkeSdGBjCX2SQ4EPA2cCJwDnJTlhHM8lSTqwcR3RbwJ2VdXXq+r7wPXA2WN6LknSAaSqRr/T5DeBM6rq94fb7wROrqoL52yzBdgy3Pw54IERj7EWeGrE+xwH5xwt5xyd1TAjvLTn/MmqWrfQRuP6mOLMs/b/fqJU1XZg+5ienyTTVTU1rv2PinOOlnOOzmqYEZxzMcZ16mY3cNyc2+uBR8f0XJKkAxhX6O8ENiZ5TZKXA+cCN4/puSRJBzCWUzdVtSfJhcCngUOBq6rqvnE81wGM7bTQiDnnaDnn6KyGGcE5FzSWX8ZKklYO3xkrSc0ZeklqztBLUnNtQp/ktUkuTnJ5kg8N14+f9FyrVZJNSd40XD8hyZ8mOWvScx1IkmsnPYMOjiQXJjli0nMcSJId+37PJJnIL2TH9YapgyrJxcB5zH7Uwh3D8nrguiTXV9VlExtuFUryPmY/p2hNkluBk4HbgG1J3lhVl05yPoAk+75cN8BbkxwOUFW/dvCnWliSX2L2I0LurarPTHqevZKcDOysqmeS/AiwDTgJuB/466p6eqIDvtCPAXcm+RJwFfDpWnmvLHkNcHGSN1XV+4e1ibxhqsWrbpL8F/C6qnpun/WXA/dV1cbJTLZ4Sc6vqo9Neg6AJPcAJwKHAY8D6+cE4ItV9fMTHRAYvsHvBz7K7LuuA1zH7Hs2qKrPTW66H0pyR1VtGq7/AbAVuBE4HfjnlXIQkuQ+4A3DS6O3A88CnwROG9Z/Y6IDziNJmP1zPJ/ZgN4AXFlVX5voYIPh7+gm4HJm30D6e8Bnq+qkgz1Ll1M3PwB+fJ71Y4b7VoP3L7zJQbOnqp6vqmeBr1XVMwBV9V1Wzp/nFHAX8JfA01V1G/DdqvrcSon84GVzrm8BfmU4ujsd+N3JjDSvQ6pqz3B9qqouqqrbh1l/apKD7c9wBP/48LUHOAL4ZJK/mehgP5Sq2lNVfwx8CrgdOGoSg7Q4dQNcBOxI8iDwyLD2E8DPABfu91EHWZK793cXcPTBnGUB30/yiiH0v7B3McmrWSGhr6ofAB9M8o/D5ROszL/Phwznkg9h9ht/BqCq/jfJngM/9KC6d86/Kr+SZKqqppP8LPDcQg8+2JK8G9jM7IeEfRT4s6p6LskhwIPAn09yvsFH9l6pqquHfylvncQgLU7dAAz/gzcBxzIbzt3AnVX1/EQHm2OI0duAb+97F/CfVTXfv0oOuiSHVdX35llfCxxTVfdMYKwDSvJ24JSqeu+kZ5kryUPM/nAMs6eYfrGqHk/ySuD2qjpxkvPtNfwQ/xDwFmbjeRKzB02PAO+uqq9McLwXSPJXzJ6m+eY89x1fVTsnMNaK1Sb0q0GSK4GPVdXt89z38ar6nQmMpQlI8grg6Kr6xqRnmSvJq5g9VbMG2F1VT0x4JI2AoZek5rr8MlaStB+GXpKaM/SS1Jyhl6Tm/g8HM0svD1agQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['职业类型'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9f445c0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['白名单客户'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9f87fd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['灰名单客户'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9fe9588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['五级分类'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
       "      <th>性别</th>\n",
       "      <th>结婚</th>\n",
       "      <th>最高学历</th>\n",
       "      <th>劳动人口数</th>\n",
       "      <th>劳动能力</th>\n",
       "      <th>是否非法</th>\n",
       "      <th>账户冻结金额</th>\n",
       "      <th>工作年限</th>\n",
       "      <th>职务</th>\n",
       "      <th>职业类型</th>\n",
       "      <th>白名单客户</th>\n",
       "      <th>灰名单客户</th>\n",
       "      <th>五级分类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15735</th>\n",
       "      <td>51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15741</th>\n",
       "      <td>56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15753</th>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15788</th>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15797</th>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       年龄   性别   结婚  最高学历  劳动人口数  劳动能力  是否非法  账户冻结金额  工作年限   职务 职业类型  白名单客户  \\\n",
       "15735  51  1.0  2.0  99.0      2   1.0   2.0     0.0   4.0  9.0    5      0   \n",
       "15741  56  1.0  2.0  60.0      2   1.0   2.0     0.0   4.0  9.0    5      0   \n",
       "15753  45  1.0  2.0  70.0      2   1.0   2.0     0.0   3.0  9.0    z      0   \n",
       "15788  41  1.0  2.0  70.0      2   1.0   2.0     0.0   4.0  9.0    z      0   \n",
       "15797  42  1.0  3.0  70.0      3   1.0   2.0     0.0   4.0  9.0    5      0   \n",
       "\n",
       "       灰名单客户  五级分类  \n",
       "15735      0     0  \n",
       "15741      0     0  \n",
       "15753      0     0  \n",
       "15788      0     0  \n",
       "15797      0     1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['五级分类']=data['五级分类'].astype('int')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns=cls_cached"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 变量类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical = ['AGE', 'WORK_SIZE', 'CURR_FREEZE_VALUE', 'GRADUATE_YEAR']\n",
    "\n",
    "categorical = ['EDU_EXPERIENCE', 'MARRIAGE', 'OCCUPATION', 'OCCUPATION_TYPE']\n",
    "\n",
    "binary = ['GENDER', 'WORK_POWER']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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